SoroushVahidi/scidocs huggingface dataset:
Monte-Carlo Tree Search for the Game of "7 Wonders"
(2014)

Monte-Carlo Tree Search algorithm, and in particular with the Upper Confidence Bounds formula, provided huge improvements for AI in numerous games, particularly in Go, Hex, Havannah, Amazons and Breakthrough. In this work we study this algorithm on a more complex game, the game of “7 Wonders”. This card game gathers together several known difficult features, such as hidden information, N-player and stochasticity. It also includes an inter-player trading system that induces a combinatorial search to decide which decisions are legal. Moreover it is difficult to hand-craft an efficient evaluation function since the card values are heavily dependent upon the stage of the game and upon the other player decisions. We show that, in spite of the fact that “7 Wonders” is apparently not so related to abstract games, a lot of known results still hold.
Collaboration, creativity and the co-construction of oral and written texts
(2009)

The Open University's repository of research publications and other research outputs Collaboration, creativity and the co-construction of oral and written texts Journal Article Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. (To be included in a special issue of Thinking Skills and Creativity, on " Collaborative Abstract In this paper we explore how primary school children 'learn to collaborate' and 'collaborate to learn' on creative writing projects by using diverse cultural artefacts-including oracy, literacy and ICT. We begin by reviewing some key socio-cultural concepts which serve as a theoretical framework for the research reported. Secondly, we describe the context in which the children talked and worked together to create their projects. This context is a 'learning community' developed as part of an innovative educational programme with the aim of promoting the social construction of knowledge among all participants. We then present microgenetic analyses of the quality of the interaction and dialogues taking place as peers worked together on their projects, and how these collaborative processes and uses of the mediational artefacts were taken up by the children. In order to exemplify these processes, our analyses centre on a selection of examples of dialogues, texts and multimedia products of stories created by groups of 4th grade (9-10 year-old) children. Overall, the work reveals the dynamic functioning in educational settings of some central socio-cultural concepts. These include: co-construction; intertextuality and intercontextuality amongst oracy, literacy and uses of ICT; collaborative creativity; development of dialogical and text production strategies and appropriation of diverse cultural artefacts for knowledge construction.
A comparative analysis of common YouTube comment spam filtering techniques
(2018)

Ever since its development in 2005, YouTube has been providing a vital social media platform for video sharing. Unfortunately, YouTube users may have malicious intentions, such as disseminating malware and profanity. One way to do so is using the comment field for this purpose. Although YouTube provides a built-in tool for spam control, yet it is insufficient for combating malicious and spam contents within the comments. In this paper, a comparative study of the common filtering techniques used for YouTube comment spam is conducted. The study deploys datasets extracted from YouTube using its Data API. According to the obtained results, high filtering accuracy (more than 98%) can be achieved with low-complexity algorithms, implying the possibility of developing a suitable browser extension to alleviate comment spam on YouTube in future.
A Novel Method for Comparative Analysis of DNA Sequences by Ramanujan-Fourier Transform
(2014)

Alignment-free sequence analysis approaches provide important alternatives over multiple sequence alignment (MSA) in biological sequence analysis because alignment-free approaches have low computation complexity and are not dependent on high level of sequence identity. However, most of the existing alignment-free methods do not employ true full information content of sequences and thus can not accurately reveal similarities and differences among DNA sequences. We present a novel alignment-free computational method for sequence analysis based on Ramanujan-Fourier transform (RFT), in which complete information of DNA sequences is retained. We represent DNA sequences as four binary indicator sequences and apply RFT on the indicator sequences to convert them into frequency domain. The Euclidean distance of the complete RFT coefficients of DNA sequences are used as similarity measures. To address the different lengths of RFT coefficients in Euclidean space, we pad zeros to short DNA binary sequences so that the binary sequences equal the longest length in the comparison sequence data. Thus, the DNA sequences are compared in the same dimensional frequency space without information loss. We demonstrate the usefulness of the proposed method by presenting experimental results on hierarchical clustering of genes and genomes. The proposed method opens a new channel to biological sequence analysis, classification, and structural module identification.
Safe Exploration of State and Action Spaces in Reinforcement Learning
(2012)

In this paper, we consider the important problem of safe exploration in reinforcement learning. While reinforcement learning is well-suited to domains with complex transition dynamics and high-dimensional state-action spaces, an additional challenge is posed by the need for safe and efficient exploration. Traditional exploration techniques are not particularly useful for solving dangerous tasks, where the trial and error process may lead to the selection of actions whose execution in some states may result in damage to the learning system (or any other system). Consequently, when an agent begins an interaction with a dangerous and high-dimensional state-action space, an important question arises; namely, that of how to avoid (or at least minimize) damage caused by the exploration of the state-action space. We introduce the PI-SRL algorithm which safely improves suboptimal albeit robust behaviors for continuous state and action control tasks and which efficiently learns from the experience gained from the environment. We evaluate the proposed method in four complex tasks: automatic car parking, pole-balancing, helicopter hovering, and business management.
Optical see-through head up displays' effect on depth judgments of real world objects
(2015)

Recent research indicates that users consistently underestimate depth judgments to Augmented Reality (AR) graphics when viewed through optical see-through displays. However, to our knowledge, little work has examined how AR graphics may affect depth judgments of real world objects that have been overlaid or annotated with AR graphics. This study begins a preliminary analysis whether AR graphics have directional effects on users' depth perception of real-world objects, as might be experienced in vehicle driving scenarios (e.g., as viewed via an optical see-through head-up display or HUD). Twenty-four participants were asked to judge the depth of a physical pedestrian proxy figure moving towards them at a constant rate of 1 meter/second. Participants were shown an initial target location that varied in distance from 11 to 20 m and were then asked to press a button to indicate when the moving target was perceived to be at the previously specified target location. Each participant experienced three different display conditions: no AR visual display (control), a conformal AR graphic overlaid on the pedestrian via a HUD, and the same graphic presented on a tablet physically located on the pedestrian. Participants completed 10 trials (one for each target distance between 11 and 20 inclusive) per display condition for a total of 30 trials per participant. The judged distance from the correct location was recorded, and after each trial, participants' confidence in determining the correct distance was captured. Across all conditions, participants underestimated the distance of the physical object consistent with existing literature. Greater variability was observed in the accuracy of distance judgments under the AR HUD condition relative to the other two display conditions. In addition, participant confidence levels were considerably lower in the AR HUD condition.
Stochastic Activation Pruning for Robust Adversarial Defense
(2018)

Neural networks have been found vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep learning systems in the wild. To guard against adversarial examples, we take inspiration from game theory. We cast the problem as a minimax zero-sum game between the adversary and the model. For such games, it is in general optimal for players to use a stochastic policy, also known as a mixed strategy. In light of this, we propose a mixed strategy for the model: stochastic activation pruning. SAP prunes a random subset of activations (preferentially pruning activations with smaller magnitude) and scales up the survivors to compensate. SAP can be applied to pre-trained neural networks—even adversarially trained models—without fine-tuning, providing robustness against adversarial examples. Experiments demonstrate that SAP confers robustness in the adversarial setting, increasing accuracy whilst preserving calibration.
Restructuring of deep neural network acoustic models with singular value decomposition
(2013)

Recently proposed deep neural network (DNN) obtains significant accuracy improvements in many large vocabulary continuous speech recognition (LVCSR) tasks. However, DNN requires much more parameters than traditional systems, which brings huge cost during online evaluation, and also limits the application of DNN in a lot of scenarios. In this paper we present our new effort on DNN aiming at reducing the model size while keeping the accuracy improvements. We apply singular value decomposition (SVD) on the weight matrices in DNN, and then restructure the model based on the inherent sparseness of the original matrices. After restructuring we can reduce the DNN model size significantly with negligible accuracy loss. We also fine-tune the restructured model using the regular back-propagation method to get the accuracy back when reducing the DNN model size heavily. The proposed method has been evaluated on two LVCSR tasks, with context-dependent DNN hidden Markov model (CD-DNN-HMM). Experimental results show that the proposed approach dramatically reduces the DNN model size by more than 80% without losing any accuracy.
Deep neural networks for small footprint text-dependent speaker verification
(2014)

In this paper we investigate the use of deep neural networks (DNNs) for a small footprint text-dependent speaker verification task. At development stage, a DNN is trained to classify speakers at the frame-level. During speaker enrollment, the trained DNN is used to extract speaker specific features from the last hidden layer. The average of these speaker features, or d-vector, is taken as the speaker model. At evaluation stage, a d-vector is extracted for each utterance and compared to the enrolled speaker model to make a verification decision. Experimental results show the DNN based speaker verification system achieves good performance compared to a popular i-vector system on a small footprint text-dependent speaker verification task. In addition, the DNN based system is more robust to additive noise and outperforms the i-vector system at low False Rejection operating points. Finally the combined system outperforms the i-vector system by 14% and 25% relative in equal error rate (EER) for clean and noisy conditions respectively.
An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices
(2015)

Detecting and reacting to user behavior and ambient context are core elements of many emerging mobile sensing and Internet-of-Things (IoT) applications. However, extracting accurate inferences from raw sensor data is challenging within the noisy and complex environments where these systems are deployed. Deep Learning -- is one of the most promising approaches for overcoming this challenge, and achieving more robust and reliable inference. Techniques developed within this rapidly evolving area of machine learning are now state-of-the-art for many inference tasks (such as, audio sensing and computer vision) commonly needed by IoT and wearable applications. But currently deep learning algorithms are seldom used in mobile/IoT class hardware because they often impose debilitating levels of system overhead (e.g., memory, computation and energy). Efforts to address this barrier to deep learning adoption are slowed by our lack of a systematic understanding of how these algorithms behave at inference time on resource constrained hardware. In this paper, we present the first -- albeit preliminary -- measurement study of common deep learning models (such as Convolutional Neural Networks and Deep Neural Networks) on representative mobile and embedded platforms. The aim of this investigation is to begin to build knowledge of the performance characteristics, resource requirements and the execution bottlenecks for deep learning models when being used to recognize categories of behavior and context. The results and insights of this study, lay an empirical foundation for the development of optimization methods and execution environments that enable deep learning to be more readily integrated into next-generation IoT, smartphones and wearable systems.
EmotionSense: a mobile phones based adaptive platform for experimental social psychology research
(2010)

Today's mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way.
 de the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale experiments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies using traditional methods. In particular, we show how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
Multimodal Deep Learning for Cervical Dysplasia Diagnosis
(2016)

To improve the diagnostic accuracy of cervical dysplasia, it is important to fuse multimodal information collected during a patient’s screening visit. However, current multimodal frameworks suffer from low sensitivity at high specificity levels, due to their limitations in learning correlations among highly heterogeneous modalities. In this paper, we design a deep learning framework for cervical dysplasia diagnosis by leveraging multimodal information. We first employ the convolutional neural network (CNN) to convert the low-level image data into a feature vector fusible with other non-image modalities. We then jointly learn the non-linear correlations among all modalities in a deep neural network. Our multimodal framework is an end-to-end deep network which can learn better complementary features from the image and non-image modalities. It automatically gives the final diagnosis for cervical dysplasia with 87.83% sensitivity at 90% specificity on a large dataset, which significantly outperforms methods using any single source of information alone and previous multimodal frameworks.
1 An End-to-End Framework for Evaluating Surface Reconstruction
(2011)

We present a benchmark for the evaluation and comparison of algorithms which reconstruct a surface from point cloud data. Although a substantial amount of effort has been dedicated to the problem of surface reconstruction, a comprehensive means of evaluating this class of algorithms is noticeably absent. We propose a simple pipeline for measuring surface reconstruction algorithms, consisting of three main phases: surface modeling, sampling, and evaluation.We employ implicit surfaces for modeling shapes which are expressive enough to contain details of varying size, in addition to preserving sharp features. From these implicit surfaces, we produce point clouds by synthetically generating range scans which resemble realistic scan data. We validate our synthetic sampling scheme by comparing against scan data produced via a commercial optical laser scanner, wherein we scan a 3D-printed version of the original implicit surface. Last, we perform evaluation by comparing the output reconstructed surface to a dense uniformly-distributed sampling of the implicit surface. We decompose our benchmark into two distinct sets of experiments. The first set of experiments measures reconstruction against point clouds of complex shapes sampled under a wide variety of conditions. Although these experiments are quite useful for the comparison of surface reconstruction algorithms, they lack a fine-grain analysis. Hence to complement this, the second set of experiments are designed to measure specific properties of surface reconstruction, both from a sampling and surface modeling viewpoint. Together, these experiments depict a detailed examination of the state of surface reconstruction algorithms. An End-to-End Framework for Evaluating Surface Reconstruction
Air-Traffic Complexity Resolution in Multi-Sector Planning
(2007)

Using constraint programming, we effectively model and efficiently solve the problem of balancing and minimising the traffic complexities of an airspace of adjacent sectors. The traffic complexity of a sector is here defined in terms of the numbers of flights within it, near its border, and on non-level segments within it. The allowed forms of complexity resolution are the changing of the take-off times of not yet airborne flights, the changing of the remaining approach times into the chosen airspace of already airborne flights by slowing down and speeding up within the two layers of feeder sectors around that airspace, as well as the changing of the levels of passage over way-points in that airspace. Experiments with actual European flight profiles obtained from the Central Flow Management Unit (CFMU) show that these forms of complexity resolution can lead to significant complexity reductions and rebalancing.
Underwater image enhancement via dark channel prior and luminance adjustment
(2016)

Underwater images are degraded mainly by light scattering and color distortion. Light scattering occurs because the light is reflected and deflected multiple times by the suspended particles in the water before reaching the camera. Color distortion is caused due to the absorption degrees vary according to the light wavelength. At last, the underwater images are dominated by a bluish tone. In this paper, we propose a novel underwater image enhancement approach based on dark channel prior and luminance adjustment. The dehazed underwater image obtained by dark channel prior characterizes with low brightness. Then, we utilize a novel luminance adjustment method to enhance image contrast. The enhanced image shows improved global contrast and better preserved image details and edges. Experimental results demonstrate the effectiveness of our method compared to the state-of-the-art methods.
Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor
(2007)

In this paper, we present a real-time algorithm for automatic recognition of not only physical activities, but also, in some cases, their intensities, using five triaxial wireless accelerometers and a wireless heart rate monitor. The algorithm has been evaluated using datasets consisting of 30 physical gymnasium activities collected from a total of 21 people at two different labs. On these activities, we have obtained a recognition accuracy performance of 94.6% using subject-dependent training and 56.3% using subject-independent training. The addition of heart rate data improves subject-dependent recognition accuracy only by 1.2% and subject-independent recognition only by 2.1%. When recognizing activity type without differentiating intensity levels, we obtain a subject-independent performance of 80.6%. We discuss why heart rate data has such little discriminatory power.
Brief Announcement: Dynamic Determinacy Race Detection for Task Parallelism with Futures
(2016)

Existing dynamic determinacy race detectors for task-parallel programs are limited to programs with strict computation graphs, where a task can only wait for its descendant tasks to complete. In this paper, we present the first known determinacy race detector for non-strict computation graphs with futures. The space and time complexity of our algorithm are similar to those of the classical SP-bags algorithm, when using only structured parallel constructs such as spawn-sync and async-finish. In the presence of point-to-point synchronization using futures, the complexity of the algorithm increases by a factor determined by the number of future operations, which includes future task creation and future get operations. The experimental results show that the slowdown factor observed for our algorithm relative to the sequential version is in the range of 1.00x to 9.92x, which is very much in line with slowdowns experienced for fully strict computation graphs.
Generalized stacking of layerwise-trained Deep Convolutional Neural Networks for document image classification
(2016)

This article presents our recent study of a lightweight Deep Convolutional Neural Network (DCNN) architecture for document image classification. Here, we concentrated on training of a committee of generalized, compact and powerful base DCNNs. A support vector machine (SVM) is used to combine the outputs of individual DCNNs. The main novelty of the present study is introduction of supervised layerwise training of DCNN architecture in document classification tasks for better initialization of weights of individual DCNNs. Each DCNN of the committee is trained for a specific part or the whole document. Also, here we used the principle of generalized stacking for combining the normalized outputs of all the members of the DCNN committee. The proposed document classification strategy has been tested on the well-known Tobacco3482 document image dataset. Results of our experimentations show that the proposed strategy involving a considerably smaller network architecture can produce comparable document classification accuracies in competition with the state-of-the-art architectures making it more suitable for use in comparatively low configuration mobile devices.